"""simple docstring"""



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:List[str]									=			{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__:str									=			{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

SCREAMING_SNAKE_CASE__:Tuple									=			{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

SCREAMING_SNAKE_CASE__:Optional[Any]									=			"""▁"""






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Any							  =						VOCAB_FILES_NAMES
 _snake_case       :					List[str]							  =						PRETRAINED_VOCAB_FILES_MAP
 _snake_case       :					List[str]							  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=False    ,		lowerCamelCase="[CLS]"    ,		lowerCamelCase="[SEP]"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase="[SEP]"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="[CLS]"    ,		lowerCamelCase="[MASK]"    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        # Mask token behave like a normal word, i.e. include the space before it and
        # is included in the raw text, there should be a match in a non-normalized sentence.
        __a       =    (
            AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase    ,		normalized=lowerCamelCase						)
            if isinstance(lowerCamelCase    ,		lowerCamelCase						)
            else mask_token
        )

        __a       =    {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            do_lower_case=lowerCamelCase    ,		remove_space=lowerCamelCase    ,		keep_accents=lowerCamelCase    ,		bos_token=lowerCamelCase    ,		eos_token=lowerCamelCase    ,		unk_token=lowerCamelCase    ,		sep_token=lowerCamelCase    ,		pad_token=lowerCamelCase    ,		cls_token=lowerCamelCase    ,		mask_token=lowerCamelCase    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**lowerCamelCase    ,		)

        __a       =    do_lower_case
        __a       =    remove_space
        __a       =    keep_accents
        __a       =    vocab_file

        __a       =    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(lowerCamelCase						)


 @property
 def 						a__   ( self						):
        return len(self.sp_model						)


 def 						a__   ( self						):
        __a       =    {self.convert_ids_to_tokens(lowerCamelCase						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab


 def __getstate__( self						):
        __a       =    self.__dict__.copy()
        __a       =    None
        return state


 def __setstate__( self    ,		lowerCamelCase						):
        __a       =    d

        # for backward compatibility
        if not hasattr(self    ,		"sp_model_kwargs"						):
               __a       =    {}

        __a       =    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(self.vocab_file						)


 def 						a__   ( self    ,		lowerCamelCase						):
        if self.remove_space:
               __a       =    " ".join(inputs.strip().split()						)
        else:
               __a       =    inputs
        __a       =    outputs.replace("``"    ,		"\""						).replace("''"    ,		"\""						)

        if not self.keep_accents:
               __a       =    unicodedata.normalize("NFKD"    ,		lowerCamelCase						)
               __a       =    "".join([c for c in outputs if not unicodedata.combining(lowerCamelCase						)]						)
        if self.do_lower_case:
               __a       =    outputs.lower()

        return outputs


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.preprocess_text(lowerCamelCase						)
        __a       =    self.sp_model.encode(lowerCamelCase    ,		out_type=lowerCamelCase						)
        __a       =    []
        for piece in pieces:
               if len(lowerCamelCase						) > 1 and piece[-1] == str(","						) and piece[-2].isdigit():
                      __a       =    self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase    ,		""						)						)
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]						) == 1:
                                    __a       =    cur_pieces[1:]
                             else:
                                    __a       =    cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]						)
                      new_pieces.extend(lowerCamelCase						)
               else:
                      new_pieces.append(lowerCamelCase						)

        return new_pieces


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.sp_model.PieceToId(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.sp_model.IdToPiece(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    []
        __a       =    ""
        __a       =    False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      if not prev_is_special:
                             out_string += " "
                      out_string += self.sp_model.decode(lowerCamelCase						) + token
                      __a       =    True
                      __a       =    []
               else:
                      current_sub_tokens.append(lowerCamelCase						)
                      __a       =    False
        out_string += self.sp_model.decode(lowerCamelCase						)
        return out_string.strip()


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        __a       =    [self.sep_token_id]
        __a       =    [self.cls_token_id]
        if token_ids_a is None:
               return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = False						):

        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase    ,		token_ids_a=lowerCamelCase    ,		already_has_special_tokens=lowerCamelCase						)

        if token_ids_a is not None:
               return [1] + ([0] * len(lowerCamelCase						)) + [1] + ([0] * len(lowerCamelCase						)) + [1]
        return [1] + ([0] * len(lowerCamelCase						)) + [1]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        __a       =    [self.sep_token_id]
        __a       =    [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep						) * [0]
        return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        if not os.path.isdir(lowerCamelCase						):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
               return
        __a       =    os.path.join(
            lowerCamelCase    ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase						) and os.path.isfile(self.vocab_file						):
               copyfile(self.vocab_file    ,		lowerCamelCase						)
        elif not os.path.isfile(self.vocab_file						):
               with open(lowerCamelCase    ,		"wb"						) as fi:
                      __a       =    self.sp_model.serialized_model_proto()
                      fi.write(lowerCamelCase						)

        return (out_vocab_file,)

"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
